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extract_features.py
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extract_features.py
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# This code extract document level features of transformer models
import os
import h5py
import pdb
from typing import List
import flair
from flair.data import Dictionary, Sentence, Token, Label
#from flair.datasets import CONLL_03, CONLL_03_DUTCH, CONLL_03_SPANISH, CONLL_03_GERMAN
import flair.datasets as datasets
from flair.data import MultiCorpus, Corpus
from flair.list_data import ListCorpus
import flair.embeddings as Embeddings
from flair.training_utils import EvaluationMetric
from flair.visual.training_curves import Plotter
# initialize sequence tagger
from flair.models import SequenceTagger
from pathlib import Path
import argparse
import yaml
from flair.utils.from_params import Params
# from flair.trainers import ModelTrainer
# from flair.trainers import ModelDistiller
# from flair.trainers import ModelFinetuner
from flair.config_parser import ConfigParser
import numpy as np
import argparse
from flair.training_utils import (
init_output_file,
WeightExtractor,
log_line,
add_file_handler,
Result,
store_embeddings,
)
def predict_embeddings(self,doc_dict,embedding,file_name):
# torch.cuda.empty_cache()
lm_file = h5py.File(file_name, "r")
for key in doc_dict:
if key == 'start':
for i, sentence in enumerate(doc_dict[key]):
for token, token_idx in zip(sentence.tokens, range(len(sentence.tokens))):
word_embedding = torch.zeros(embedding.embedding_length).float()
word_embedding = torch.FloatTensor(word_embedding)
token.set_embedding(embedding.name, word_embedding)
continue
group = lm_file[key]
num_sentences = len(list(group.keys()))
sentences_emb = [group[str(i)][...] for i in range(num_sentences)]
try:
assert len(doc_dict[key])==len(sentences_emb)
except:
pdb.set_trace()
for i, sentence in enumerate(doc_dict[key]):
for token, token_idx in zip(sentence.tokens, range(len(sentence.tokens))):
word_embedding = sentences_emb[i][token_idx]
word_embedding = torch.from_numpy(word_embedding).view(-1)
token.set_embedding(embedding.name, word_embedding)
store_embeddings([sentence], 'cpu')
parser = argparse.ArgumentParser('extract_features.py')
parser.add_argument('--config', help='configuration YAML file.')
parser.add_argument('--test', action='store_true', help='Whether testing the pretrained model.')
parser.add_argument('--zeroshot', action='store_true', help='testing with zeroshot corpus.')
parser.add_argument('--all', action='store_true', help='training/testing with all corpus.')
parser.add_argument('--other', action='store_true', help='training/testing with other corpus.')
parser.add_argument('--quiet', action='store_true', help='print results only')
parser.add_argument('--nocrf', action='store_true', help='without CRF')
parser.add_argument('--parse', action='store_true', help='parse files')
parser.add_argument('--parse_train_and_dev', action='store_true', help='chech the performance on the training and development sets')
parser.add_argument('--keep_order', action='store_true', help='keep the parse order for the prediction')
parser.add_argument('--predict', action='store_true', help='predict files')
parser.add_argument('--debug', action='store_true', help='debugging')
parser.add_argument('--target_dir', default='', help='file dir to parse')
parser.add_argument('--spliter', default='\t', help='file dir to parse')
parser.add_argument('--recur_parse', action='store_true', help='recursively parse the file dirs in target_dir')
parser.add_argument('--parse_test', action='store_true', help='parse the test set')
parser.add_argument('--save_embedding', action='store_true', help='save the pretrained embeddings')
parser.add_argument('--mst', action='store_true', help='use mst to parse the result')
parser.add_argument('--test_speed', action='store_true', help='test the running speed')
parser.add_argument('--predict_posterior', action='store_true', help='test the running speed')
parser.add_argument('--batch_size', default=32, type=int, help='set the mini batch size for extraction')
parser.add_argument('--window_size', default=511, type=int, help='transformer window_size')
parser.add_argument('--stride', default=1, type=int, help='transformer stride')
parser.add_argument('--keep_embedding', default=-1, help='mask out all embeddings except the index, for analysis')
args = parser.parse_args()
config = Params.from_file(args.config)
configparser = ConfigParser(config,all=args.all,zero_shot=args.zeroshot,other_shot=args.other,predict=args.predict)
corpus = configparser.corpus
config = configparser.config
trainer = config['trainer']
embeddings, word_map, char_map, lemma_map, postag_map=configparser.create_embeddings(config['embeddings'])
corpus2id = {x:i for i,x in enumerate(corpus.targets)}
doc_sentence_dict = {}
for corpus_id in range(len(corpus2id)):
corpus_name = corpus.targets[corpus_id].lower()+'_'
doc_name = 'train_'
doc_idx = -1
for sentence in corpus.train_list[corpus_id]:
if '-DOCSTART-' in sentence[0].text:
doc_idx+=1
doc_key='start'
else:
doc_key=corpus_name+doc_name+str(doc_idx)
if doc_key not in doc_sentence_dict:
doc_sentence_dict[doc_key]=[]
doc_sentence_dict[doc_key].append(sentence)
doc_name = 'dev_'
doc_idx = -1
for sentence in corpus.dev_list[corpus_id]:
if '-DOCSTART-' in sentence[0].text:
doc_idx+=1
doc_key='start'
else:
doc_key=corpus_name+doc_name+str(doc_idx)
if doc_key not in doc_sentence_dict:
doc_sentence_dict[doc_key]=[]
doc_sentence_dict[doc_key].append(sentence)
doc_name = 'test_'
doc_idx = -1
for sentence in corpus.test_list[corpus_id]:
if '-DOCSTART-' in sentence[0].text:
doc_idx+=1
doc_key='start'
else:
doc_key=corpus_name+doc_name+str(doc_idx)
if doc_key not in doc_sentence_dict:
doc_sentence_dict[doc_key]=[]
doc_sentence_dict[doc_key].append(sentence)
for idx, embedding in enumerate(embeddings.embeddings):
if embedding.name not in config[trainer]['pretrained_file_dict']:
continue
output_file = config[trainer]['pretrained_file_dict'][embedding.name]
writer = h5py.File(output_file, 'a')
for doc_id, doc_key in enumerate(doc_sentence_dict):
if doc_key!='start':
# pdb.set_trace()
sentences=embedding.add_document_embeddings(doc_sentence_dict[doc_key], window_size=args.window_size, stride=args.stride, batch_size = args.batch_size)
# pdb.set_trace()
# ====================================== debug =========================================
# lm_file = h5py.File('../temp/biaffine-ner/bert_features.hdf5', "r")
# group = lm_file['train_0']
# num_sentences = len(list(group.keys()))
# sentences_emb = [group[str(i)][...] for i in range(num_sentences)]
# idx=-1
# sentfeat=np.concatenate([sentences_emb[idx][:,:,i] for i in range(sentences_emb[idx].shape[-1])],-1)
# for i in range(len(sentences_emb[idx])): np.absolute(sentences[idx][i].embedding.cpu().numpy()-sentfeat[i]).max()
# pdb.set_trace()
# ====================================== debug =========================================
file_key = doc_key.replace('/', ':')
for sentence_index, sentence in enumerate(sentences):
dataset_key ="{}/{}".format(file_key, sentence_index)
if dataset_key not in writer:
writer.create_dataset(dataset_key,
(len(sentence), embedding.embedding_length),
dtype=np.float32)
dset = writer[dataset_key]
for token_id, token in enumerate(sentence):
dset[token_id, :] = token.embedding.cpu().numpy()
store_embeddings(sentences,'none')
if (doc_id+1) % (len(doc_sentence_dict)//10) == 0:
print(f'Processed {doc_id+1}/{(len(doc_sentence_dict))} documents')
# writer = h5py.File(FLAGS.output_file, 'w')
# with tqdm(total=sum(len(e.tokens) for e in orig_examples)) as t:
# for result in estimator.predict(input_fn, yield_single_examples=True):
# document_index = int(result["unique_ids"])
# bert_example = bert_examples[document_index]
# orig_example = orig_examples[document_index]
# file_key = bert_example.doc_key.replace('/', ':')
# t.update(n=(result['extract_indices'] >= 0).sum())
# for output_index, bert_token_index in enumerate(result['extract_indices']):
# if bert_token_index < 0:
# continue
# token_index = bert_example.bert_to_orig_map[bert_token_index]
# sentence_index, token_index = orig_example.unravel_token_index(token_index)
# dataset_key ="{}/{}".format(file_key, sentence_index)
# if dataset_key not in writer:
# writer.create_dataset(dataset_key,
# (len(orig_example.sentence_tokens[sentence_index]), bert_config.hidden_size, len(layer_indexes)),
# dtype=np.float32)
# dset = writer[dataset_key]
# for j, layer_index in enumerate(layer_indexes):
# layer_output = result["layer_output_%d" % j]
# dset[token_index, :, j] = layer_output[output_index]
# writer.close()